Visual search patterns, information selection strategies, and information anxiety for online information problem solving

Meng Jung Tsai*, An Hsuan Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Online information problem solving (OIPS) is essential for 21st century information literacy which often requires information selection competencies. However, students usually have problems in discriminating information from complex web sources. This study, utilizing eye-tracking technology, aims to examine the relationships among learners' visual search patterns, information anxiety and OIPS task performance in web search contexts. In this study, 46 university students volunteered to participate in a web search task for solving a landslide problem. Student’ visual behaviors were recorded by eye-trackers during the task and information anxiety was self-reported immediately after the task. Reaction time and task performance were also recorded and scored. Pearson's correlation analyses, multiple regression analyses, cluster analyses and lag sequential analyses (LSA) were conducted. The results show that learners' eye-tracking measures, information anxiety, and task performances are significantly correlated in OIPS. Students' visual attention paid onto irrelevant web information can significantly and positively predicts their information anxiety, but negatively predicts their task performance. Additionally, based on eye-tracking measures and reaction time, three visual search patterns are identified: Confused, Slow-thinking and Fast-thinking. The LSA results further show that different information selection and attentional control strategies are utilized by different groups, especially when discriminating the relevancy of web information. This study bridges the associations among eye-tracking measures, information anxiety and task performances in OIPS contexts. Future studies are suggested to analyze eye-tracking data using cluster analyses plus LSAs to profile online learners' characteristics in terms of visual search or information processing patterns.

Original languageEnglish
Article number104236
JournalComputers and Education
Publication statusPublished - 2021 Oct


  • Data science applications in education
  • Eye-tracking
  • Human computer interaction
  • Information literacy
  • Teaching/learning strategies

ASJC Scopus subject areas

  • General Computer Science
  • Education


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